Quality Management and Improvement

NASTAD Prevention and Care

Technical Assistance Meeting 2016

Iowa Part B

Quality

Management

Program

NASTAD Prevention

and Care TA Meeting

July 29, 2016

Holly Hanson, MA

Part B Program Manager

Katie Herting

RW Quality Coordinator

Topics

• Overview of the Ryan White Part B

Quality Management (QM) Program

– QM Plan

– QM Team

• Performance measure results highlights

for 2015/16

• Next steps

Overheard at the Meeting

QM Plan

1. Quality Statement

2. Organizational Infrastructure

3. Performance Measurement System

4. Implementation Plan

5. Annual Quality Goals

6. Evaluation

7. Capacity Building

8. Updating the QM Plan

QM Plan – Performance Measures

QM Team

• Meets in-person quarterly

• Conference calls as needed

• Responsibilities:

– Determine performance measures

– Design continuous quality improvement activities

– Review the QM Plan annually

QM Team

• In 2015/16:– Selected increasing real-time entry of housing

status in CAREWare as a CQI project

– Revised performance measures:• Added

– Women Iowans Living with HIV with a Suppressed Viral Load

– Women RW Part B Clients with a Suppressed Viral Load

– Percent of MSM Tested for Syphilis at Three Iowa RW Part C Clinics

– Churn Within the ADAP

• Discontinued – ADAP Client Fill Rate

– Turnover of Ryan White Staff

– Retention of Ryan White Staff

September 2016• Approve the QM Plan for the 2016/17

year

• Review and approve draft of the 2015/16

Annual Report

• Discuss new performance measures that

should be added

• Data collection for new measures

would begin January 1, 2017

QM Team – Time Cycle

June 2017• Review new data (that was

collected in March) through

subcommittee presentation of

updated fact sheets

• QM Team self-assessment

• Discuss any necessary

revisions to the QM Plan for

the 2017/18 year

December 2016• Plan CQI projects to be

implemented in early 2017

• QM Program assessment of the

2015/16 year

March 2017• QM training - different topic each year

• Discuss if current performance measures

should be continued in the 2017/18 year

• Divide the team into subcommittees for

fact sheets

2016/17

Result Highlights

• On-Time ADAP Recertification

Result Highlights

• Iowans & RW Part B Clients with a

Suppressed Viral Load

Result Highlights

• Churn Within the ADAP

Result Highlights

• CAREWare vs. FTE CQI Project

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Pe

rce

nt

Mat

ch

All Agency Average Percent Match

Baseline Average = 27%CQI Project

Average = 50%

Result Highlights

• RW Part B Retention in Care

90% 92% 90%

Result Highlights

• Annual Syphilis Screenings in MSM

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

University ofIowa

Project ofPrimary

Healthcare

SiouxlandCommunity

Health Center

TOTAL

16%

82% 83%

46%40%

92%

81%

63%

29%

72%

40%46% 2014

2015

2016 *

*2016 data is preliminary and

measured from 1/1/2016 – 7/22/2016

Result Highlights

• Viral Suppression by Age

64%

77%

85%

76%

63%

78%

87%82%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

13-24 25-44 >45 Total

Iowans Living with HIV RW Part B Clients

_

Next Steps

• Produce second Annual QM Report

• Design and implement continuous

quality improvement projects

• Incorporate RW Part C, Prevention, and

STD into the QM Program

• Develop individual contractor QM

Reports

Questions?

Katie Herting

RW Quality Coordinator

Katie.Herting@idph.iowa.gov

Holly Hanson, MA

Part B Program Manager

Holly.hanson@idph.iowa.gov

19

Improvements in Data Quality across the

HIV Continuum of Care: Timeliness,

Accuracy and Completeness

VIRGINIA DEPARTMENT OF HEALTH

Division of Disease Prevention

Anne Rhodes, PhD

Director, HIV Surveillance

20

Data Quality: What, Why, How?

• Surveillance data is no longer just utilized for funding

formulas and epi profiles

• Real-time tracking of diagnosis, linkage, care

engagement, medication adherence and viral

suppression are needed

• Current data systems – set up artificially with barriers

based on funding streams, jurisdictions, disease status,

etc.

21

Defining the HIV Continuum of Care

Linkage Retention Viral Suppression

Evidence of a care

marker within

30/90 days of

initial HIV

diagnosis

2 or more care

markers in 12 months

at least 3 months

apart

Last viral load <200

copies/mL in the time

period being measured

What’s considered a care marker?

CD4 testViral load

testHIV medical

care visitART

prescription

22

Timeliness

• NHAS – 4th Goal calls

to “strengthen the

timely availability

and use of data”

• Viral suppression

rates for 2013 for

persons living with

HIV as of 12/31/2012

released by CDC in

July 2016

23

Black Box: Real Time HIV Surveillance

• Pilot project from Georgetown, funded by NIH

• Involved DC, MD, and VA Departments of Health

• Utilized privacy technology for sharing surveillance

data among jurisdictions where an algorithm for

matching was set up in the “black box” and returned

matches of varying strengths (Exact to Very Low) to

each jurisdiction

24

Black Box ResultsOutput of person-matching across DC, MD, and VA

eHARS databases:

Person

matches

across

jurisdictions:

Exact Very

High High

Medium

High Medium

Very

Low Total

DC-MD* 4013 5907 53 268 645 482 11 368

MD-VA* 856 2343 11 117 377 865 4569

VA-DC* 1064 3340 15 149 438 529 5535

Total 5933 11 590 79 534 1460 1876 21 472

*Bidirectional reporting results (i.e., DC-reported MD matches were equal to MD-reported DC-1

matches; etc.) 2

Over half of matches were

not known to jurisdictions 24

25

Completeness

• Markers for care cannot all be tracked in eHARS

• Systems outside of health department purview often

have data on care status for PLWH

• Electronic medical records/health information

exchanges/all payer claims databases often available in

jurisdictions

26

Care Markers Database: Sources

Care Markers

VACRS/

E2VA/CW: RW Labs,

Med Visits, ART dates ADAP:

Labs, ART, Med visits

HIV Testing:

Testing and Demo

Info

MMP:

Med Visits, ART, CD4s,

VLseHARS:

CD4s, VLs, Demographic Info, Address

Info, Vital Status

STD*MIS

Address Info

Medicaid:

Fee-for-Service Lab, Med Visit, ART Dates

Accurint: Vital Status and Address

Info

27

e2Virginia

Ryan White All Parts dataHIV Prevention data (Corrections, CAPUS,

Testing)

Patient Navigation Process Data

ADAP Recertification/Medications

Surveillance Lab Data

e2Virginia

28

29

30

70%

56%

43% 42%

78%

100%

89%

69%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Persons diagnosedand living with HIVas of 12/31/2015

Diagnosed in 2015and linked to HIV

care within 30days

Evidence of HIVcare in 2015

Retained in HIVcare in 2015

Virally suppressedin 2015

Persons living with HIV in Virginia as of 12/31/2015 (N=24,853)

Persons served by Ryan White 2015 (N=10,058)

Virginia HIV Continuum of Care, 2015

31

Accuracy

• How do people get included in/excluded from

Continuum of Care analyses?

• Death

• Proof of out of jurisdiction address

• No care in xx period of time?

• Modeling methods?

• Only care in xx period of time?

24% of current living cases in eHARS – no lab in last

5 years (n=6,005)

32

Overall DtC Outcomes

N=192

Data reported to the Virginia Department of Health as of 06/09/2016

Deceased4%

Discharged1%

In Care56%

Incarcerated2%

Not in Care5%

Other3%

Relocated OOS12%

Unable to be Located

17%

33

70%

56%

43% 42%

100%

81%

68%

53% 51%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Personsdiagnosed and

living with HIV asof 12/31/2015

Diagnosed in 2015and linked to HIV

care within 30days

Evidence of HIVcare in 2015

Retained in HIVcare in 2015

Virally suppressedin 2015

Pre-LexisNexis Accurint match: Persons living with HIV in Virginia as of

12/31/2015 (N=24,853)

Persons diagnosed with HIV in Virginia in 2015 (N=929)

10,706

Virginia HIV Continuum of Care, 2015: Pre and Post-

LexisNexis Accurint Match

Data current as of December 2015; Accessed July 2016; Virginia Department of Health, Division of Disease Prevention.

Data for 2015 should be considered preliminary and may be incomplete due to reporting delay. LexisNexis Accurint batch match as of July 2016; post-

LexisNexis Accurint HIV care continuum only includes persons with a last known residence in Virginia or diagnosed in Virginia (linkage to care) and

considered living in both HIV surveillance and in the LexisNexis Accurint batch match were included

Post-LexisNexis Accurint match: Persons living with HIV in Virginia as of

12/31/2015 (N=18,870)

Persons diagnosed with HIV in Virginia in 2015 (N=825)

9,97610,350

9,556

13,945

12,816

754

581

24,853

18,870

3434

Improved Accuracy of Case Numbers

• After address and vital status updates, number of PLWHliving in Virginia as of 12/31/2015 was reduced by 760 persons

Increased Number of Care Markers for

Continuum

• Black Box, along with other sources, including Medicaid and Ryan White added 8% to retention rates in 2014 and 9% to viral suppression rates in 2015

Virginia Results: So Far

35

Final Thoughts

• Data Improvement strategies should be part of plan for

addressing NHAS goals

• Sharing data across jurisdictions is important for

tracking linkage, care engagement and viral

suppression for PLWH

• Utilizing data for public health impact requires merging

of multiple sources of information across systems,

agencies, and funding streams

36

Acknowledgements

CDC: Benjamin Laffoon, Dr. Irene Hall

DC Department of Health: Michael Kharfen, Garret Lum, Auntre Hamp

Georgetown University: Jeff Collman, Joanne Michelle Ocampo, Jay

Smart, Raghu Pemmaraju

HRSA: Jessica Xavier, John Hannay

Maryland Department of Health: Colin Flynn, Reshma Bhattacharjee

RDE Systems: Jesse Thomas, Anusha Dayananda, RDE Developer Team

Virginia Department of Health: Anne Rhodes, Jeff Stover, Steve Bailey,

Elaine Martin, Lauren Yerkes, Kate Gilmore, Sahithi Boggavarapu,

Sonam Patel, Amanda Saia